class_docstr: Strategy for training with multiple processes in parallel.
docstr: Return the root device.
docstr_tok: ["Return","the","root","device","."]
code: def root_device(self) -> torch.device:
      """Return the root device."""
  
imports: from abc import ABC, abstractmethod
  from contextlib import contextmanager
  from typing import Any, Dict, Generator, List, Optional
  import torch
  from torch import Tensor
  from typing_extensions import override
  import lightning.pytorch
  from lightning.fabric.plugins import CheckpointIO, ClusterEnvironment
  from lightning.fabric.utilities.distributed import ReduceOp, _all_gather_ddp_if_available
  from lightning.pytorch.plugins import LayerSync
  from lightning.pytorch.plugins.precision import Precision
  from lightning.pytorch.strategies.strategy import Strategy
label_desc: Use image node_id 2 for calling the ParallelStrategy obj's underlying member method code with example usage: obj.root_device() without return types